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Generalized Multilevel Functional Regression
Journal of the American Statistical Association (2009)
  • Ciprian M Crainiceanu, Johns Hopkins University
  • Ana-Maria Staicu, North Carolina State University
  • Chong-Zhi Di, Fred Hutchinson Cancer Research Center

We introduce Generalized Multilevel Functional Linear Models (GMFLMs), a novel statistical framework for regression models where exposure has a multilevel functional structure. We show that GMFLMs are, in fact, generalized multilevel mixed models. Thus, GMFLMs can be analyzed using the mixed effects inferential machinery and can be generalized within a well-researched statistical framework. We propose and compare two methods for inference: (1) a two-stage frequentist approach; and (2) a joint Bayesian analysis. Our methods are motivated by and applied to the Sleep Heart Health Study, the largest community cohort study of sleep. However, our methods are general and easy to apply to a wide spectrum of emerging biological and medical datasets. Supplemental materials for this article are available online.

  • Functional principal components; Sleep EEG; Smoothing
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Citation Information
Ciprian M Crainiceanu, Ana-Maria Staicu and Chong-Zhi Di. "Generalized Multilevel Functional Regression" Journal of the American Statistical Association Vol. 104 Iss. 488 (2009)
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